Gain powerful insights with our interactive traffic data visualization tool, designed to highlight congestion trends across San Francisco, on both weekdays and weekends. The intuitive map lets you explore the dataset for free and easily identify peak hours and busy zones.

Create a free account to unlock advanced analysis features and compare traffic patterns over time. Whether you're an urban planner, researcher, or part of an OOH advertising team, this tool helps you make data-driven decisions by pinpointing high-traffic areas with precision.

The Smart Mobility and Traffic Optimization Dataset integrates data from cyber-physical networks (CPNs) and social networks (SNs) to improve traffic management and smart mobility solutions. By combining real-time traffic patterns, vehicle telemetry, ride-sharing demand, public transport efficiency, social media sentiment, and environmental factors, this dataset provides a comprehensive foundation for optimizing urban mobility.

Designed to support machine learning models, the dataset enables accurate predictions of traffic congestion, mobility optimization, and smart city planning. It incorporates key metrics such as vehicle density, road occupancy, weather conditions, social media feedback, and emissions data to generate actionable insights.

Key Features: Traffic Data: Includes vehicle count, speed, road occupancy, and traffic light status, offering a granular view of real-time traffic conditions. Weather & Accidents: Integrates weather conditions and accident reports to assess their impact on congestion levels. Social Network Sentiment: Analyzes public opinions and complaints about mobility and congestion, extracted from social media platforms. Smart Mobility Factors: Examines ride-sharing demand, parking availability, and public transport delays, aiding in urban mobility planning. Environmental Impact: Monitors CO₂ emissions and pollution levels, ensuring eco-friendly traffic optimization. Target Variable: The dataset categorizes traffic congestion levels into three main groups: Low, Medium, or High, based on real-time traffic density, speed, and road occupancy.

This dataset is an essential resource for urban planners, smart city developers, and AI researchers, empowering them to create intelligent mobility solutions that reduce congestion, enhance efficiency, and improve overall urban sustainability.

Gain powerful insights with our interactive traffic data visualization tool, designed to highlight congestion trends across London, on both weekdays and weekends. The intuitive map lets you explore the dataset for free and easily identify peak hours and busy zones.

Create a free account to unlock advanced analysis features and compare traffic patterns over time. Whether you're an urban planner, researcher, or part of an OOH advertising team, this tool helps you make data-driven decisions by pinpointing high-traffic areas with precision.

Traffic Data as a Service Market Outlook

According to our latest research, the global Traffic Data as a Service market size reached USD 1.58 billion in 2024, driven by the increasing demand for real-time traffic analytics and the growing adoption of smart transportation solutions worldwide. The market is poised to expand at a robust CAGR of 18.2% from 2025 to 2033, with the total market value forecasted to reach approximately USD 7.22 billion by the end of 2033. This remarkable growth trajectory is attributed to the rapid urbanization, increasing investments in intelligent transportation infrastructure, and the need for data-driven solutions to optimize traffic flow and reduce congestion.

One of the primary growth factors propelling the Traffic Data as a Service market is the exponential rise in urban population, leading to increased vehicular density and complex traffic patterns in metropolitan areas. Governments and municipal authorities are under mounting pressure to manage traffic congestion, reduce environmental impact, and enhance commuter safety. The integration of advanced analytics, artificial intelligence, and IoT-enabled sensors within traffic management systems has enabled the collection, processing, and analysis of vast amounts of traffic data in real time. This technological evolution is not only facilitating efficient route planning and congestion management but also providing actionable insights for infrastructure development and urban mobility planning.

Another significant driver for the Traffic Data as a Service market is the surge in demand from logistics and fleet operators for accurate, real-time, and predictive traffic data. With the global supply chain becoming increasingly complex, logistics companies are leveraging traffic data services to optimize delivery routes, minimize fuel consumption, and improve overall operational efficiency. Additionally, the proliferation of ride-hailing and last-mile delivery services has heightened the need for precise traffic information to enhance customer satisfaction and reduce operational costs. The integration of predictive analytics and machine learning algorithms further empowers these operators to anticipate traffic disruptions and proactively adjust their strategies.

The increasing focus on smart city initiatives and digital transformation across both developed and emerging economies is also fueling the growth of the Traffic Data as a Service market. Governments are investing heavily in intelligent transportation systems (ITS) to improve urban mobility, ensure public safety, and achieve sustainability goals. The deployment of cloud-based traffic data platforms and the adoption of open data policies are fostering collaboration between public and private stakeholders, thereby accelerating the development and implementation of innovative traffic management solutions. The convergence of 5G connectivity, edge computing, and big data analytics is expected to further amplify the capabilities and adoption of traffic data services in the coming years.

From a regional perspective, North America currently dominates the Traffic Data as a Service market owing to its advanced transportation infrastructure, early adoption of smart city technologies, and significant investments in digital mobility solutions. However, the Asia Pacific region is anticipated to exhibit the fastest growth during the forecast period, driven by rapid urbanization, increasing vehicle ownership, and government initiatives aimed at modernizing transportation networks. Europe also represents a substantial market share, supported by stringent regulations on traffic management and sustainability, as well as robust public-private partnerships in the transportation sector.

Component Analysis

The Component segment of the Traffic Data as a Service market is broadly categorized into Software, Services, and Platforms. Each component plays a critical role in the overall value chain, offering unique capabilities and addressing diverse customer requirements. Software solutions encompass traffic analytics platforms, visualization tools, and data integration modules that enable organizations to process and interpret vast volumes of traffic data. These software tools are increasingly being enhanced with artificial intelligence and machine learning algorithms, enabling predictive analytics and real-time decision-making. As cities and enterprises seek to harness the power of data-driven insights, the

Gain powerful insights with our interactive traffic data visualization tool, designed to highlight congestion trends across Paris, on both weekdays and weekends. The intuitive map lets you explore the dataset for free and easily identify peak hours and busy zones.

Create a free account to unlock advanced analysis features and compare traffic patterns over time. Whether you're an urban planner, researcher, or part of an OOH advertising team, this tool helps you make data-driven decisions by pinpointing high-traffic areas with precision.

US Automatic Traffic Recorder Stations Data

Vehicle Traffic Counts and Locations at US ATR Stations

By Homeland Infrastructure Foundation [source]

About this dataset

This comprehensive dataset records important information about Automatic Traffic Recorder (ATR) Stations located across the United States. ATR stations play a crucial role in traffic management and planning by continuously monitoring and counting the number of vehicles passing through each station.

The data contained in this dataset has been meticulously gathered from station description files supplied by the Federal Highway Administration (FHWA) for both Weigh-in-Motion (WIM) devices and Automatic Traffic Recorders. In addition to this, location referencing data was sourced from the National Highway Planning Network version 4.0 as well as individual State offices of Transportation.

The database includes essential attributes such as a unique identifier for each ATR station, indicated by 'STTNKEY'. It also indicates if a site is part of the National Highway System, denoted under 'NHS'. Other key aspects recorded include specific locations generally named after streets or highways under 'LOCATION', along with relevant comments providing additional context in 'COMMENT'.

Perhaps one of the most critical factors noted in this data set would be traffic volume at each location, measured by Annual Average Daily Traffic ('AADT'). This metric represents total vehicle flow on roads or highways for a year divided over 365 days — an essential numeric analyst's often call upon when making traffic-related predictions or decisions.

Location coordinates incorporating longitude and latitude measurements of every ATR station are documented clearly — aiding geospatial analysis. Furthermore, X and Y coordinates correspond to these locations facilitating accurate map plotting.

Additional information contained also includes postal codes labeled as 'STPOSTAL' where stations are located with respective state FIPS codes indicated under ‘STFIPS’. County specific FIPS code are documented within ‘CTFIPS’. Versioning information helps users track versions ensuring they work off latest datasets with temporal geographic attribute updates captured via ‘YEAR_GEO’.

Reference Source: Click Here

How to use the dataset

Introduction

Diving into the data

The dataset comprises a collection of attributes for each station such as its location details (latitude, longitude), AADT or The Annual Average Daily Traffic amount, classification of road where it's located etc. Additionally, there is information related to when was this geographical information last updated.

Understanding Columns

Here's what primary columns represent: - Sttnkey: A unique identifier for each station. - NHS: Indicates if the station is part of national highway system. - Location: Describes specific location of a station with street or highway name. - Comment: Any additional remarks related to that station. - Longitude,Latitude: Geographic coordinates. - STPostal: The postal code where a given station resides. - menu 4 dots indicates show more items** - ADT: Annual Average Daily Traffic count indicating average volume of vehicles passing through that route annually divided by 365 days - Year_GEO: The year when geographic information was last updated - can provide insight into recency or timeliness of recorded attribute values - Fclass: Road classification i.e interstate,dis,e tc., providing context about type/stature/importance or natureof theroad on whichstationlies 11.Stfips,Ctfips- FIPS codes representing state,county respectively

Using this information

Given its structure and contents,thisdatasetisveryusefulforanumberofpurposes:

1.Urban Planning & InfrastructureDevelopment Understanding traffic flows and volumes can be instrumental in deciding where to build new infrastructure or improve existing ones. Planners can identify high traffic areas needing more robust facilities.

2.Traffic Management & Policies Analysing chronological changes and patterns of traffic volume, local transportation departments can plan out strategic time-based policies for congestion management.

3.Residential/CommercialRealEstateDevelopment Real estate developers can use this data to assess the appeal of a location based on its accessibility i.e whether it sits on high-frequency route or is located in more peaceful, low-traffic areas etc

4.Environmental AnalysisResearch: Re...

Traffic Count Viewer is an online mapping application, which users can use to explore traffic count reports in different locations within the Delaware Valley, including Philadelphia. Users search by location (address, city, zip code, or place name) to view point features on the interactive mapping visualization of traffic records. Clicking on a point of interest or grouping multiple points on the map yields traffic count information tables, which includes: Date of Counnt ; DVRPC File # ; Type ; Annual Average Daily Traffic (AADT) ; Municipality ; Route Number ; Road Name ; Count Direction ; and From/To Locations, as well as a link to the detailed (hourly) report. Data tables are exportable as .CSV and detailed reports are available for export in multiple formats (including basic .doc and .rtf outputs.) Traffic count data is collected by the Delaware Valley Regional Planning Commission and other agencies.

The Flight Data Visualization System (FDVS) market is booming, projected to reach $2.579 billion by 2025 and grow at a CAGR of 6.1% through 2033. Learn about key market drivers, trends, and top players shaping this dynamic sector. Explore regional market analysis and future projections for aviation data visualization.

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

Gain powerful insights with our interactive traffic data visualization tool, designed to highlight congestion trends across Madrid, on both weekdays and weekends. The intuitive map lets you explore the dataset for free and easily identify peak hours and busy zones.

Create a free account to unlock advanced analysis features and compare traffic patterns over time. Whether you're an urban planner, researcher, or part of an OOH advertising team, this tool helps you make data-driven decisions by pinpointing high-traffic areas with precision.

Snapshot img

Integrated Traffic Systems Market Size 2025-2029

The integrated traffic systems market size is forecast to increase by USD 22.92 billion, at a CAGR of 14.8% between 2024 and 2029.

The market is driven by the escalating demand for efficient traffic management in response to the increasing number of passenger vehicles on the roads worldwide. This trend is further fueled by the growing issue of road traffic congestion, which negatively impacts urban mobility and productivity. However, the market faces significant challenges. The high setup cost and operating cost associated with implementing integrated traffic systems can act as a barrier to entry for potential market entrants. Despite these challenges, the market offers opportunities for companies to innovate and provide cost-effective solutions that address the pressing need for effective traffic management.
Companies that successfully navigate these challenges and deliver solutions that enhance urban mobility and reduce congestion are poised to capture a significant share in this growing market.

What will be the Size of the Integrated Traffic Systems Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is characterized by its continuous evolution and dynamic nature, with various entities interplaying to optimize traffic flow and enhance road safety. Traffic simulation modeling and pedestrian signals work in tandem to anticipate and manage foot traffic, while traffic monitoring systems and traffic control software ensure real-time data collection and analysis. Traffic signal foundations and signal timing adjustment maintain the infrastructure's stability and efficiency, with vehicle detection sensors and traffic signal poles facilitating seamless communication between components. Network management systems and traffic data visualization enable effective centralized traffic control, integrating traffic accident data, signal timing plans, and traffic violation detection.

Traffic signal optimization and coordination are essential for congestion management, with roadway capacity analysis and dynamic message signs providing valuable insights. Traffic data acquisition and traffic incident management are crucial for maintaining optimal traffic flow, while traffic signal installation and maintenance ensure the longevity and reliability of the systems. Moreover, emerging technologies such as automated traffic enforcement, emergency vehicle preemption, and variable speed limits are transforming the landscape of traffic management, offering innovative solutions for traffic flow analysis and traffic signal hardware. Intersection design and traffic volume counts continue to evolve, incorporating the latest advancements in video image processing and traffic signal controllers. The integration of these entities fosters a comprehensive, adaptive traffic management ecosystem, addressing the ever-changing demands of modern transportation infrastructure.

How is this Integrated Traffic Systems Industry segmented?

The integrated traffic systems industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Solution

  Traffic monitoring system
  Traffic control system
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

.

By Solution Insights

The traffic monitoring system segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to the increasing demand for efficient and effective traffic management solutions. Traffic monitoring is a crucial aspect of these systems, enabling traffic analysts to identify patterns and address issues such as congestion, inefficient routing, and poor road conditions. Traffic monitoring systems, like those offered by SWARCO, provide real-time observations, traffic operation monitoring, and video management. The rising urbanization rates in developing countries, where traffic personnel may be scarce, further emphasize the importance of these systems. Additionally, advanced technologies such as loop detectors, traffic violation detection, and traffic signal optimization contribute to the market's expansion.

The integration of network management systems, traffic data collection, and traffic incident management also enhances the overall functionality and effectiveness of these systems. Furthermore, the implementation of centralized traffic control, traffic signal coordination, and real-time traffic m

This dataset captures key factors influencing traffic accidents in both urban and rural areas it provides detailed information about environmental infrastructural and behavioral variables that are crucial for understanding the dynamics of road safety with a focus on 8756 observations it covers a wide range of scenarios from dense urban intersections to quieter rural roads the number of recorded traffic accidents ranges from minor incidents to significant collisions the traffic fine amount represents the average amount of traffic fines in thousands of USD in the observed area linked to enforcement efforts and driver behavior traffic density is represented by a score indicating the volume of vehicles in the area on a scale from 0 low to 10 high the proportion of traffic lights in the area highlights intersections with varying levels of control pavement quality is rated from 0 to 5 with higher values indicating better infrastructure there is a binary indicator showing whether the area is urban 1 or rural 0 the dataset also captures the typical speed of vehicles in kilometers per hour representing driving conditions rain intensity is measured on a scale from 0 no rain to 3 heavy rain emphasizing the role of weather in accidents the estimated number of vehicles in thousands present in the area during the observation is also included the dataset uses a 24-hour format from 0 to 24 to capture temporal patterns in accident occurrences this dataset is designed for traffic safety analysis urban planning and infrastructure improvement predictive modeling to identify high-risk conditions and prevent accidents and policymaking to enhance road safety and reduce traffic-related incidents researchers urban planners and policymakers can analyze trends to identify temporal and spatial patterns of accidents develop machine learning models to predict accident hotspots prioritize areas needing better pavement quality or traffic control and understand the role of weather speed and traffic density in accident rates the dataset is entirely fictitious and has been created for educational and illustrative purposes only it does not represent real-world data and should not be used for decision-making or policy implementation without validation against actual data sources it is intended to demonstrate analytical methods and modeling techniques in the context of traffic safety.

Weather & Traffic Data Widgets Market Outlook

According to our latest research, the global Weather & Traffic Data Widgets market size reached USD 3.62 billion in 2024, reflecting robust adoption across multiple industries. The market is expected to expand at a CAGR of 11.7% from 2025 to 2033, reaching a forecasted market size of USD 10.08 billion by 2033. This impressive growth is primarily driven by increasing demand for real-time data integration, the proliferation of smart devices, and the rising importance of data-driven decision-making in urban mobility and logistics sectors.

The surge in demand for Weather & Traffic Data Widgets is being propelled by the rapid digitalization of transportation and logistics operations worldwide. As businesses and governments strive to optimize routes, minimize delays, and enhance safety, there is a growing reliance on real-time weather and traffic data. This data enables predictive analytics, allowing for proactive measures in response to changing environmental conditions. Furthermore, the integration of these widgets into fleet management solutions, navigation systems, and smart city infrastructure is streamlining operations and reducing operational costs. The expansion of IoT and connected vehicles is also fostering the adoption of advanced data widgets, further fueling market growth.

Technological advancements are another key growth driver in the Weather & Traffic Data Widgets market. The evolution of machine learning, artificial intelligence, and big data analytics has greatly enhanced the accuracy and utility of weather and traffic data widgets. These technologies enable the processing and visualization of vast data streams, providing actionable insights for end-users. Moreover, the increasing availability of high-speed internet and 5G networks is facilitating seamless data transmission, making real-time updates more accessible and reliable. As a result, both public and private sector organizations are investing heavily in the deployment of sophisticated widgets to support critical decision-making processes.

The growing emphasis on smart city initiatives globally is significantly contributing to the widespread adoption of Weather & Traffic Data Widgets. Urban planners and municipal authorities are incorporating these widgets into city infrastructure to monitor congestion, predict adverse weather impacts, and inform citizens in real time. The integration of such widgets into consumer electronics, such as smartphones and wearables, is also expanding the market’s reach. Additionally, the media and entertainment sector is leveraging these tools to provide dynamic, location-based content, further diversifying application areas and revenue streams.

Regionally, North America currently dominates the Weather & Traffic Data Widgets market due to its advanced technological landscape, high adoption of smart city solutions, and strong presence of leading industry players. Europe follows closely, driven by stringent regulations regarding road safety and environmental monitoring. The Asia Pacific region is expected to witness the fastest growth, supported by rapid urbanization, increasing investments in smart infrastructure, and the proliferation of connected devices. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, fueled by digital transformation efforts and growing awareness of the benefits of real-time data integration.

Component Analysis

The Weather & Traffic Data Widgets market is segmented by component into software, hardware, and services. Software solutions constitute a significant portion of the market due to their pivotal role in data aggregation, analytics, visualization, and integration with various platforms. These software widgets are designed to seamlessly embed within web and mobile applications, offering real-time weather and traffic information to users. The increasing adoption of SaaS-based models and cloud-native applications is further propelling the growth of software components. Developers and enterprises are prioritizing customizable and scalable solutions to meet the evolving needs of end-users, ensuring that software remains at the forefront of market expansion.

Hardware components encompass sensors, display units, and embedded systems that collect, process, and present weather and traffic data. The pro

Data Cleaning:

Cleaning the data is required prior to studying the dataset. This stage entails locating and correcting flaws in the Wireshark dataset, such as missing or null values and inconsistent data.

`network_traffic_data = pd.read_csv('/MidTerm_19_group.csv', delimiter=',', encoding='utf-8') network_traffic_data.head()

Removing rows with any null values

network_traffic_data = network_traffic_data.dropna()`

Exploratory0Data Analysis (EDA):

The first step in gaining a thorough understanding of the dataset's properties is to analyze it. Investigating the dataset's dimensions, composition, and variable types is necessary for this. Then, we use data visualization tools, including making charts and plots, to identify patterns and trends in the dataset. We can also easily identify any potential outliers with the use of visualization.

`# Shape of the DataFrame network_traffic_data.shape

columns of the DataFrame

network_traffic_data.columns

Output: (4013629, 7) Index(['No.', 'Time', 'Source', 'Destination', 'Protocol', 'Length', 'Info'], dtype='object') `

Snapshot img

Intelligent Traffic Management Market Size 2025-2029

The intelligent traffic management market size is forecast to increase by USD 24.01 billion at a CAGR of 14.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for advanced, AI-based traffic solutions. This demand is driven by the escalating number of vehicles on the road and the resulting need for more efficient and effective traffic management systems. However, the market faces challenges as well. The lack of skilled professionals in government traffic organizations poses a significant barrier to the implementation and maintenance of these complex systems. Despite these challenges, the market presents numerous opportunities for companies seeking to capitalize on the growing demand for intelligent traffic management solutions.
Green traffic lights, on-demand transportation, and shared mobility services are also gaining popularity, contributing to the evolution of the traffic management infrastructure. Strategic partnerships, collaborations, and investments in research and development are key strategies for companies looking to stay competitive in this dynamic market. By addressing the skills gap and continuing to innovate, companies can help ensure the successful implementation and adoption of intelligent traffic management systems, ultimately improving traffic flow, reducing congestion, and enhancing public safety.

What will be the Size of the Intelligent Traffic Management Market during the forecast period?

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The market in the United States is experiencing significant growth, driven by the increasing demand for next-generation traffic management solutions. Traffic safety technologies, such as real-time traffic information, dynamic traffic routing, and pedestrian detection systems, are becoming essential components of the smart mobility ecosystem. The integration of traffic data acquisition and data-driven traffic management is revolutionizing urban traffic management, leading to road safety improvement and sustainable transportation. Traffic management innovation continues to shape the industry, with a focus on transportation network analysis, traffic data visualization, and traffic congestion mitigation.
Intelligent parking management and traffic incident detection are essential components of the market, ensuring efficient and safe traffic flow. The market is also witnessing the emergence of mobility-as-a-service (MaaS) platforms, which are transforming the way people move around cities. The market's growth is further fueled by the development of traffic management standards and the increasing adoption of data-driven approaches. The trend towards sustainable traffic management is also influencing the market, with a focus on reducing carbon emissions and improving overall transportation efficiency. In summary, the market in the United States is a dynamic and rapidly evolving industry, driven by the demand for next-generation traffic management solutions and the integration of data-driven approaches. The market's growth is underpinned by the need for improved traffic operations management, sustainable transportation, and the development of a smart mobility ecosystem.

How is the Intelligent Traffic Management Industry segmented?

The intelligent traffic management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Solution

  Traffic monitoring system
  Traffic signal control system
  Traffic enforcement camera
  Integrated corridor management
  Others


Component

  Surveillance cameras
  Video walls
  Traffic controllers and signals
  Others


End-user

  Government authorities
  Transport agencies
  Commercial


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Solution Insights

The traffic monitoring system segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, particularly in the Traffic Monitoring Systems segment. By 2029, this segment is expected to evolve substantially, integrating advanced sensor technologies, video analytics, and real-time data processing frameworks. These systems will shift from reactive to proactive approaches, utilizing predictive analytics algorithms to anticipate congestion patterns and optimize signal timings dynamically. IoT-enabled devices and edge computing architectures will facilitate faster data transmission and localized decision-making, minimizing latency in traffic management operations. Furthermore, multimodal transportation data, including pub

Displays vehicle traffic volumes for arterial streets in Seattle based on spot studies that have been adjusted for seasonal variation. Data is a one time snapshot for 2007 and is maintained by Seattle Department of Transportation. Contact: Traffic Operations Refresh Cycle: None, Snapshot for 2007 Only.

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

Python tools to read, generate, and visualize the dataset,

3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

Benjamin Sertolli: Major contributions to our DevKit during his master thesis

Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

1. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

traffic signs,

traffic lights,

pole-like objects,

construction site locations,

construction site obstacles (point-like such as cones, and line-like such as fences),

line-shaped markings (solid, dashed, etc.),

polygon-shaped markings (arrows, stop lines, symbols, etc.),

lanes (ordinary and temporary),

relations between elements (only for construction sites, e.g., sign to lane association).

1. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

1. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

x-coordinates: 4 byte integer

y-coordinates: 4 byte integer

z-coordinates: 4 byte integer

intensity of reflected beams: 2 byte unsigned integer

ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

Citizen Traffic Data Exchange Platforms Market Outlook

According to our latest research, the citizen traffic data exchange platforms market size reached USD 2.14 billion in 2024 globally. The market is experiencing robust momentum, supported by a compound annual growth rate (CAGR) of 14.2% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 6.19 billion. This growth is primarily driven by the increasing adoption of smart city initiatives, growing urbanization, and the rising need for real-time traffic data to optimize mobility and urban planning.

The expansion of the citizen traffic data exchange platforms market is largely attributed to the proliferation of connected devices and the integration of Internet of Things (IoT) technologies in urban infrastructure. As metropolitan areas continue to experience population growth, the demand for efficient traffic management solutions becomes more pressing. These platforms aggregate and analyze data from a multitude of sources, including mobile applications, sensors, and GPS-enabled devices, to provide actionable insights for both citizens and authorities. The ability to harness real-time and predictive traffic data not only enhances commuter experiences but also supports city planners in making data-driven decisions that reduce congestion and improve overall urban mobility.

Another significant growth factor is the increasing collaboration between public and private sectors in the development and deployment of advanced traffic data solutions. Governments and transportation authorities are increasingly recognizing the value of citizen-contributed data in enhancing situational awareness and optimizing transportation networks. Investments in digital infrastructure, coupled with policy frameworks that encourage data sharing, are catalyzing the adoption of these platforms. Furthermore, advancements in artificial intelligence and machine learning are enabling more sophisticated analytics, empowering stakeholders to anticipate traffic patterns, manage emergencies, and implement dynamic traffic controls with greater precision.

The growing emphasis on sustainability and environmental management is also fueling the adoption of citizen traffic data exchange platforms. As cities strive to reduce carbon emissions and promote greener mobility options, the ability to monitor and manage traffic flows becomes critical. These platforms enable the identification of congestion hotspots, facilitate the optimization of public transportation routes, and support the deployment of eco-friendly mobility solutions. The integration of historical, real-time, and predictive traffic data is instrumental in achieving long-term urban sustainability goals, making these platforms an essential component of modern urban ecosystems.

From a regional perspective, North America and Europe are currently leading the market, driven by early adoption of smart city technologies and significant investments in digital infrastructure. However, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, increasing government initiatives for smart cities, and the widespread use of mobile devices. Latin America and the Middle East & Africa are also witnessing growing interest, though adoption rates vary due to infrastructural and regulatory challenges. Overall, the global outlook for citizen traffic data exchange platforms remains highly positive, with substantial opportunities for growth across both developed and emerging markets.

Component Analysis

The citizen traffic data exchange platforms market is segmented by component into software, hardware, and services. The software segment constitutes the backbone of these platforms, encompassing data aggregation, analytics, visualization, and integration tools. Software solutions are evolving rapidly, with vendors focusing on the development of intuitive interfaces, robust data processing capabilities, and seamless integration with third-party applications. The increasing deployment of cloud-based software solutions is enabling real-time data sharing and collaboration among stakeholders, thereby enhancing the overall efficiency and scalability of traffic management systems. Additionally, the emergence of open APIs and modular software architectures is allowing cities to customize and extend platform functionalities to suit their unique requirements.

A file with preprocessed data of traffic flow simulation and GraphML file describing the simulated area of the traffic network. This data is used for the example visualization of traffic flow by IT4Innovations/FlowMapFrame package.

traffic index data for Saudi Arabia in Middle east cities along with Cairo. Dataset includes traffic index, jams counts, jams lengths in an hourly frequency. This dataset can be merged with IBM weather data and vacation calendar, and can be analyzed to understand the reasonings behind the traffic.